https://doi.org/10.65294/gpogc.2026.08
Forecasting the risk of formation damage
(colmatation) with a 14-day horizon using calibrated probabilistic models H.Kh.
Malikov, T.E. Abdulmutalibov, G.V. Jabbarova, R.R. Tashmenov
Formation damage (colmatation) is a major contributor to production losses and increased operating expenditures, yet routine diagnostics are often reactive and provide limited lead time for low-cost corrective actions. This study develops a reproducible 14-day early-warning workflow that forecasts well-calibrated event probabilities (not only risk rankings) and links them to an economically justified operating threshold. The target event is defined as a productivity-index decline below an engineering limit, IC = Q0 fact/ Q0 theor < 0.90, occurring within a 14-day horizon. Daily operational time series (water cut, net GOR, wellhead pressure, choke setting, GLR, and production rates) are transformed into a leakage-safe dynamic feature set using lags, rolling statistics, and trend descriptors computed strictly from information available at the forecast time. Model development follows rolling-origin (time-aware) backtesting with out-of-fold probability calibration (Platt scaling and isotonic regression). Performance is assessed in terms of discrimination (PR-AUC/ROC-AUC) and calibration (Brier score, expected calibration error, and calibration plots), while the operating threshold 𝜏𝜏∗is chosen by minimizing expected decision cost under asymmetric error penalties and validated via decision-curve and lift analyses. The calibrated models provide operationally meaningful probabilities that align with empirical event rates across temporal windows, and the selected 𝜏𝜏∗yields positive net benefit compared with treat-all/treat-none baselines. The proposed pipeline is suitable for integration into a decision-support workflow with explicit recommendations for temporal validation, calibration, cost-based thresholding, and drift monitoring.